Currently many financial institutions and investors use a range of completely different computer models and systems to analyse and evaluate different types of assets or securities. (Assets or securities are defined herein in the broadest possible terms, including, for example; shares, bonds, convertible instruments, call options, put options, futures, swaps, credit default swaps, other derivatives, other financial contracts, real assets, financial assets, liabilities, indices, commodities etc. Similarly references to securities issued by, or referenced to, a firm also refers to securities issued by, or referenced to, any other underlying asset.) Not only is the efficacy of many of these models in doubt (for example, the empirical performance of the Capital Asset Pricing Model and the Black-Scholes option pricing model have both been critiqued in numerous studies), but significant computing resources are also required to run multiple models.
Furthermore, not only are different models currently used for different types of assets, but multiple models are often required for the same asset type. For example, after fitting the Black-Scholes option pricing model to observed option prices, by way of solving for the implied volatilities, a second model is then typically required to model the resulting three dimensional implied volatility surface in order to price other options that might be written on the same underlying asset (see, for example, the volatility surface fitting procedure described in Dumas, B., Fleming, J. and Whaley, R. E. Journal of Finance, Implied Volatility Functions: Empirical Tests, 1998, 53(6), 2059-2106, and FIG. 1). In the case of this example, a single option pricing model that could parsimoniously fit option prices to observed market prices (i.e. explain the Black-Scholes implied volatility surface) would eliminate the need to run the second model with consequential savings in the required computer resources, a reduction in the possibility of modelling errors and faster processing times. In a real-time trading environment the latter two technical effects are particularly important.
In the case of equity securities (for example, stocks or shares) a range of models are typically used in their analysis. These models include the Capital Asset Pricing Model, shown in FIG. 2, the Fama-French three factor model and the Arbitrage Pricing Theory. While in the case of debt-type securities a different suite of models is typically applied. For example, the Merton option-theoretic model or the reduced form model. In the case of the Merton option-theoretic model, applied in a risk neutral world, it is known in the art that the resulting probability of default estimates are not “real world” estimates. Hence a second model is then typically required to “map” the risk neutral default probability estimates to real world default probabilities, as shown in FIG. 3 and as, for example, applied by commercial service provider Moodys KMV.
The invention introduces the use of risk adjusted discount rates, incorporating a risk premium or premia, into the modelling of security or asset prices or values. In the case of option pricing, the calculation of a single risk adjusted discount rate to value an option has widely been considered to be extremely difficult, if not impossible.
The risk adjusted discounting approach to valuing options was recognised by Nobel Prize winning economist Samuelson (in Samuelson, P. A. Rational Theory of Warrant Pricing, Industrial Management Review, 1965, 6(2), 13-32), who allowed for a risk adjusted rate of return on the underlying asset (a) and a different risk adjusted rate of return on the option (β) but he did “ . . . not pretend to give a theory from which one can deduce the relative values of α and β” (pp. 19-20).
Economists Merton and Scholes received Nobel Prizes for their work in pricing options in a risk neutral framework. The press release at the time of their award noted the difficulty in trying to apply a risk premium approach:                “The value of an option to buy or sell a share depends on the uncertain development of the share price to the date of maturity. It is therefore natural to suppose—as did earlier researchers—that valuation of an option requires taking a stance on which risk premium to use, in the same way as one has to determine which risk premium to use when calculating present values in the evaluation of a future physical investment project with uncertain returns. Assigning a risk premium is difficult, however, in that the correct risk premium depends on the investor's attitude towards risk. Whereas the attitude towards risk can be strictly defined in theory, it is hard or impossible to observe in reality.” (http://nobelprize.org/economics/laureates/1997/press.html)        
While the general concept of equating the price of risk across different securities is known in the art, to date the focus has been on pricing exposure to economy-wide or state variables (e.g. the stock market, GDP growth, oil prices etc.). Furthermore, the traditional view has been that the risk premium implicit in the expected returns on debt-type securities is related to factors such as liquidity or taxes, on the basis that there is little, if any, non-diversifiable residual risk exposure from investing in corporate bonds. Models that have attempted to relate the risk/return of the firm's debt and equity securities typically require additional computing resources and/or have been poorly specified through, for example, mixing instantaneous measures of volatility with discrete time measures of return. For example, the focus of the prior art in this area has been on analysis of instantaneous relationships of securities' risk and return using the first derivatives of pricing model equations, typically applied under risk neutral pricing assumptions. As noted by Galai and Masulis (in Galai, D. and Masulis, R. W. The Option Pricing Model and the Risk Factor of Stock, Journal of Financial Economics, 1976, 3, 53-81) a problem with using instantaneous measures of the volatility of securities in an option-theoretic model of the firm is that such volatility measures are not stationary through time (i.e. through the life of the “option”). The shortcomings of the prior art are solved in preferred embodiments of the invention.
In summary, while there have been attempts to introduce a coherent framework for analysing different types of securities, or assets, to date none have been able to achieve a parsimonious and efficacious approach that results in a reduction in the databases, models and computing resources required by users.